47
4.1.2 Within Community Analyses
All the villages manifested statistically significant relationships with at least one of the project participation or knowledge indices tables 19 and 20, and at least one village dummy variable is found in each
statistically significant regression equation. These statistically significant correlations may be the result of inter-village differences in the predictor variables and or relationships between the predictor variables and the
indices. As a first step in exploring these differences, we will examine the distribution of the predictor variables across the four villages. The result of this analysis for education, age, and family size can be found in Table 21.
Table 21 indicates that respondents in the sample from Talise have the lowest levels of education, while those from Bentenan have the
highest. Blongko has the lowest household
size and Tumbak the largest. The inter-
village differences are statistically significant
for both of these variables. Age does not
vary significantly across the four project villages.
Inter-village differences in relative importance of fishing and farming to household income can be found in tables 22 and 23. In these tables, rank 1 indicates the indicated occupation is ranked first in
importance, 2 indicates second in importance, and 3 indicates third or less in importance. Zero indicates that the indicated occupation does not contribute to household income.
The villages differ quite a bit with respect to the distribution of the
relative importance of fishing. Most community members in both Tumbak
and Talise depend on fishing as the first or second most important source of
household income. Bentenan and Blongko depend the least on fishing.
These intercommunity differences are statistically significant.
Bentenan and Tumbak depend least on farming for household income.
Tumbak depends the least, and Blongko depends the most on farming.
The inter-villages differences in dependence on farming are also
statistically significant.
Finally, turning to inter- community differences in
religious preference, we find that Tumbak has the greatest
percentage of Muslims and Blongko the least. Bentenen
and Talise are approximately evenly split between
Christians and Muslims. These inter-village differences
in religious preference are statistically significant table 24.
Table 22. Percent distribution of relative importance of fishing to household income across project villages.
Bentenan Tumbak Blongko
Talise None
40.0 7.5
45.0 17.5
Rank 2 12.5 7.5
27.5 10.0
Rank=2 12.5
35.0 17.5
25.0 Rank=1
35.0 50.0
10.0 47.5
Total 100
100 100
100 N
80 80
80 80
χ
2
=92.272 df=9 p0.001
Table 23. Percent distribution of relative importance of farming to household income across project villages.
Bentenan Tumbak Blongko Talise Total N
None 40.0
87.5 7.5
22.5 39.4
126 Rank 2
17.5 7.5
27.5 27.5
20.0 64
Rank=2 15.0
5.0 25.0
30.0 18.8
60 Rank=1
27.5 0.0
40.0 20.0
21.9 70
Total 100 100
100 100
100 N
80 80
80 80
320 χ
2
=131.025 df=9 p0.001
Table 24. Percent distribution of religion across project villages.
Bentenan Tumbak
Blongko Talise
Total N
Islamic 45.0
100.0 10.0
48.8 50.9
163 Christian
55.0 0.0
90.0 51.3
49.1 157
Total 100
100 100
100 100
N 80
80 80
80 320
χ
2
=131.984 df=3 p0.001
Table 21. Analysis of inter-village differences in age, education, and family size.
Bentenan Tumbak Blongko Talise F-ratio p
Education years 7.64
7.16 7.20
5.95 4.468
0.002 Age
41.29 38.85
41.93 42.93
1.464 0.224
Household size 4.45
5.25 4.03
4.78 8.354
0.001 N
80 80
80 80
df=3 316 for all F-ratio analyses
48 The inter-village differences with respect to the predictor variables suggest that intra-village analyses
of the project participation and knowledge information may enhance our understanding of both inter-individual and inter-village differences with respect to implementation of project activities. The following sections
examine these relationships within each project village.
Bentenan:
Correlations between independent variables and
project participation and knowledge indices for
Bentenan are in table 25. Within the Bentenan sample,
which has the highest overall number of years of formal
education table 9, education is only related to the MPA
Knowledge Index. This contrasts with the total sample
analysis where education is related to all the indices. However, the strength of the correlation is relatively high. Relationships between gender and project participation are stronger in Bentenan than the total sample,
suggesting that some factors influence more male participation in the village. None of the relationships between age and the four indices are statistically significant, which is similar to the total sample where only one
index is statistically significantly related to age, but at a very low level.
Religion manifests a relatively strong relationship with the two project participation indices in
Bentenan, the negative relationship with Christian indicating that Muslims participate more. It is interesting
that we find similar relationships between importance of farming to household income and the two participation
indices. However, the relationships between the importance of fishing and the two participation indices
are in the opposite direction. This suggests that there may be a relationship between religious preference and
the relative importance of fishing and farming to household income. The analyses presented in tables 26
and 27 support this hypothesis. The correlation between religious preference and the importance of farming to
household income is relatively strong contingency coefficient, C=0.58 and statistically significant—
Christians are more likely to depend on farming than Muslims. The relationship is in the opposite direction
for fishing—Muslims are more likely to depend on fishing than Christians. Finally, household size
manifests no statistically significant relationships with any of the four indices. It is interesting to note that none
of the independent variables are statistically significantly correlated with the Project Knowledge Index.
Once again, step-wise multiple regression was used to determine the combinations of independent variables that impact project participation and knowledge. The analyses were conducted only for indices
statistically significantly related to at least two of the independent variables. The results of these analyses can be found in table 28. Table 28 indicates that three of the independent variables—education, gender, and
religious preference—account for 38 percent of the variance in the Project Participation Index. The zero-order correlation between education and the Project Participation Index was not statistically significant table 25, but
Table 25. Correlations between independent variables and project participation and knowledge indices in Bentenan.
Participa- tion index
Participa- tion level
Project knowledge
index MPA
knowledge index
Education 0.169
0.145 0.163
0.457 Gender male
0.397 0.257
-0.010 0.324
Age -0.009
-0.097 -0.103
-0.176 Christian
-0.453 -0.385
-0.217 -0.162
Household size 0.121
0.065 0.177
0.060 Fishing
0.237 0.376
-0.009 0.113
Farming -0.268
-0.322 -0.213
-0.092 N=80 =p0.001 =p0.01 =p0.05
Table 26. Percent distribution of relative importance of fishing to household income
across household religious preference.
Islamic Christian
Total N
None 11.1
77.3 47.5
38 Rank 2
22.2 18.2
20.0 16
Rank=2 33.3
4.5 17.5
14 Rank=1
33.3 0.0
15.0 12
Total 100
100 100
N 36
44 80
χ
2
=42.452 df=3 p0.001 C=0.59
Table 27. Percent distribution of relative importance of farming to household income
across household religious preference.
Islamic Christian Total N
None 72.2
13.6 40.0
32 Rank 2
22.2 13.6
17.5 14
Rank=2 5.6
22.7 15.0
12 Rank=1
0.0 50.0
27.5 22
Total 100
100 100
N 36
44 80
χ
2
=39.716 df=3 p0.001 C=0.58
49 when the effects of religious preference
are controlled, the partial correlation increases to 0.25 p=0.02. When the
effects of gender are also controlled, the correlation increases only slightly 0.26;
p=0.02, but the probability is still less than 0.05; hence, education is entered
into the regression equation. Two variables, gender and religious preference
account for 19 percent of the variance in the Participation Level Index. Finally,
education, gender, and religious preference account for almost one-third
32 percent of the variance in the MPA knowledge index—a modest, but
respectable amount.
Tumbak: Correlations between
independent variables and project participation and knowledge indices for
Tumbak are in table 29. Religious preference is not an independent variable
in the Tumbak sample because all respondents are Muslim. Formal education is related to the Project Participation Index and both the project knowledge indices. The relationship is positive indicating that more
education results in greater project participation and knowledge. Gender is positively related to the Project Participation and MPA Knowledge Indices, indicating, once again, that some factors influence more male
involvement in project activities. The Participation Level Index is statistically significantly related to only one independent variable in
Tumbak—relative importance of fishing to household
income. In Tumbak, the more important fishing is, the lower
the level of participation. Finally, age, household size,
and relative dependence on farming are not related to any
of the project knowledge or participation indices in
Tumbak.
Stepwise multiple regression was used to determine the combinations of
independent variables that impact project participation and knowledge in Tumbak.
The analyses were conducted only for indices statistically significantly related to
at least two of the independent variables. The results of these analyses can be found
in table 30. As expected from our examination of the zero-order correlation
analyses, education and gender are the principal determinants of project
participation and knowledge in Tumbak. These two variables, together, account for
21 and 17 percent of the variance in the Project Participation and MPA
Table 28. Stepwise regression analyses of project partic- ipation and knowledge indices in Bentenan N=80.
DEPENDENT VARIABLE: PARTICIPATION INDEX STANDARDIZED
INDEPENDENT VARIABLE BETA COEFF. PROB. Education
0.207 0.023 Gender male
0.387 0.001 Christian -0.478 0.001
R=0.64 R
2
=0.41 Adj. R
2
=0.38 F=17.245 p 0.001 DEPENDENT VARIABLE: PARTICIPATION LEVEL
STANDARDIZED INDEPENDENT VARIABLE BETA COEFF. PROB.
Gender male 0.257 0.013
Religion -0.385 0.001 R=0.46 R
2
=0.21 Adj. R
2
=0.19 F=10.483 p 0.001 DEPENDENT VARIABLE: MPA KNOWLEDGE
STANDARDIZED INDEPENDENT VARIABLE BETA COEFF. PROB.
Education 0.468 0.001
Gender male 0.302 0.002
Christian -0.217 0.023 R=0.59 R
2
=0.35 Adj. R
2
=0.32 F=13.462 p 0.001
Table 29. Correlations between independent variables and project participation and knowledge indices in Tumbak.
Participa- tion index
Participa- tion level
Project knowledge
index MPA
knowledge index
Education 0.354
-0.092 0.226
0.314 Gender male
0.338 -0.132
0.032 0.310
Age 0.159
0.084 -0.082
-0.111 Household size
-0.001 -0.110
0.087 -0.049
Fishing -0.048
-0.251 0.038
0.029 Farming
-0.031 0.049
-0.012 0.044
N=80 =p0.01 =p0.05
Table 30. Stepwise regression analyses of project partic- ipation and knowledge indices in Tumbak N=80.
DEPENDENT VARIABLE: PARTICIPATION INDEX STANDARDIZED
INDEPENDENT VARIABLE BETA COEFF. PROB. Education
0.340 0.001 Gender male 0.323 0.002
R=0.48 R
2
=0.23 Adj. R
2
=0.21 F=11.496 p 0.001 DEPENDENT VARIABLE: MPA KNOWLEDGE
STANDARDIZED INDEPENDENT VARIABLE BETA COEFF. PROB.
Education 0.301 0.004
Gender male 0.297 0.005 R=0.43 R
2
=0.19 Adj. R
2
=0.17 F=8.837 p 0.001
50 Knowledge Indices, respectively. These relatively modest findings are statistically significant.
Blongko: Correlations
between independent variables and project participation and
knowledge indices for Blongko are in table 31. Blongko differs
from the other project villages in terms of the usefulness of the
independent variables for predicting project participation
and knowledge. Only one independent variable—degree
of household dependence on fishing—is statistically
significantly correlated with any of the project participation and knowledge indices. And it is only correlated with one—the Project Participation Level Index. Since only one independent variable is related to the indices
no regression analyses are conducted.
Talise: Two of the sub-villages of Talise are
located on a very small island just offshore from the main island of Talise. These sub-villages, known as
Kinabohutan, are very distinct in terms of a differential emphasis on fishing and farming tables 32 and 33 and
religious preference. Kinahobutan places less emphasis on farming, more on fishing, and 97.5 percent of its
inhabitants are followers of Islam while the respondents from other sub-villages of Talise are 100 percent
Christian. Because of these differences, we have used Kinabohutan as a dummy variable in our analyses to see
if there are any differences that can be attributed to the cultural differences between these distinct sectors of the
village of Talise.
Correlations between independent variables and project participation and knowledge indices for Talise are
in table 34. It is revealing that in Talise, where we found the lowest average years of formal education table 21,
education appears to be the most important predictor of project participation and knowledge. Education is
significantly p0.001 related to all four indices, indicating that respondents with higher levels of
education tend to participate and know more about Proyek
Pesisir. Gender is significantly related to the Project
Participation and Knowledge Indices, once again indicating
that in some villages there are factors that result in males
tending to participate and know more about the project.
Finally, in contrast to Blongko and Bentenan, and similar to
Tumbak, the degree that fishing contributes to
Table 32. Percent distribution of relative importance of fishing to household income
Talise Dusun 1 and 2 versus Kinabohutan
Talise Kinabo-
hutan Total
N None
35 17.5
14 Rank 2
20 10.0
8 Rank=2
30 20
25.0 20
Rank=1 15
80 47.5
38 N
40 40
80 χ
2
=40.589 df=3 p0.001 C=0.58
Table 33. Percent distribution of relative importance of farming to household income
Talise Dusun 1 and 2 versus Kinabohutan
Talise Kinabo-
hutan Total
N None
15 30
22.5 18
Rank 2 30
25 27.5
22 Rank=2
20 40
30.0 24
Rank=1 35
5 20.0
16 N
40 40
80 χ
2
=13.848 df=3 p=0.003 C=0.38
Table 34. Correlations between independent variables and project participation and knowledge indices in Talise.
Participa- tion index
Participa- tion level
Project knowledge
index MPA
knowledge index
Education 0.402
0.426 0.429 0.391
Gender male 0.307
0.093 0.304
0.184 Age
-0.004 -0.089
-0.051 -0.006
Christian 0.193
0.367 0.009
0.244 Kinabohutan
-0.207 -0.400
0.021 -0.254
Household size 0.046
0.193 0.072
0.031 Fishing
-0.158 -0.314
0.131 0.006
Farming 0.132
0.055 0.043
0.120 N=80 =p0.001 =p0.01 =p0.05
Table 31. Correlations between independent variables and project participation and knowledge indices in Blongko.
Participa- tion index
Participa- tion level
Project knowledge
index MPA
knowledge index
Education 0.193
0.097 0.177
0.194 Gender male
0.067 -0.044
0.078 0.175
Age 0.086
0.070 0.040
-0.148 Christian
0.086 0.186
0.055 -0.093
Household size 0.155
0.049 0.038
-0.066 Fishing
0.180 0.262
-0.043 0.119
Farming 0.147
0.114 -0.059
-0.039 N=80 =p0.05
51 household income is negatively related to the Project Participation Level Index. It should be noted that the
correlations of Kinabohutan with the indices are almost the mirror image of the correlations with Christian e.g., one is negative where the other is positive at similar levels. This is due to the differences in religious
preference between Kinabohutan and the rest of Talise; hence, it will be impossible to separate the effects of these two variables.
Once again, stepwise multiple regression is used to determine the
combinations of independent variables that impact project participation and knowledge
in Talise. Results of these analyses are in table 35. Education is once again the
principal predictor of the project participation and knowledge indices. There
is no multiple regression presented for the MPA Knowledge Index due to the fact that
once education was entered into the regression equation, the partial correlations
for religious preference and Kinabohutan reduced to 0.16 p0.05; hence, they were
not entered into the equation in the stepwise process. Once again the analyses indicate
that males and those with higher education tend to participate in and know about the
project. The relative importance of fishing to household income also influences
knowledge about the project. Overall the multiple regressions are modest, but
statistically significant.
4.2 Changes in Material Style Of Life